Welcome to the American College of Toxicology s Webinar Series. We will begin at 11AM EDT

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Beryl Goodwin
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1 Welcome to the American College of Toxicology s Webinar Series We will begin at 11AM EDT

2 Preparing for Nonclinical edata Regulatory Submissions to the US FDA SEND and Beyond September 5, 2013, 11:00 AM EDT/3:00 PM GMT Co-Sponsored by: Society of Toxicologic Pathology

3 Presenters Timothy J. Kropp, PhD, Team Leader, Tools Technology and Innovation Team, Office of Computational Science, CDER US Food and Drug Administration Shree Nath, PhD, Vice President, PointCross Life Sciences Slide 3

4 SEND (Standard for Exchange of Nonclinical Data) Processes, tools, and training in place for reviewers to use SEND submitted data Update on SEND submissions to date. Slide 4

5 Validation rules for nonclinical datasets available The Validation Rules for SEND Formatted Studies is an Excel file that provides human readable description of a rule set for validation. Submitters of non-clinical study data can use this information to identify how FDA will validate submitted data. Available from the FDA Study Data Standards Resources webpage: astandards/default.htm. Slide 5

6 Validation rules The file contains a combination of conformance rules (i.e. how well the data conform to the standard) and business rules (i.e. quality checks; how well the data may support meaningful analysis). The file may be updated periodically as new or updated validation rules are developed. The Change History tab will provide a descriptive change history of the document. Slide 6

7 Technical conformance guidance Common errors over time will be consolidated to inform guidance given to sponsors Slide 7

8 Test submissions and questions Highly recommended Participation in pre-ind/nda/bla meetings Contact before submission or with any questions Slide 8

9 Topics Nonclinical e-data Standardization & Submissions History Considerations for Regulatory e-data Submissions What have we learnt from FDA NIMS e-data submissions and from industry implementations? Future Directions: Innovative ways to standardize studies for submission into NIMS: Medical Countermeasures (MCM) as an example Protocol Design to drive downstream data collection & e-data preparation Q&A Slide 9

10 Nonclinical e-data Standardization & Submissions History SEND Initiation Informal Pilot SEND 2.3 IG CRADA w/ PointCross for viewing & validation software (ToxVision) CDER Regulatory Pilot General Tox, Carc Studies SEND V3 IG Safety Pharm, ReproTox INHAND/CDISC/FDA Collaboration on Controlled Terminologies Draft Guidance NIMS is the FDA s Nonclinical Information Management System NIMS G0-Live MCM stands for Medical Countermeasures used against CBRN Threat Agents. MCMs can be approved by the FDA without clinical trials under the Animal Model Rule. MCM Models Slide 10

11 Components of an e-data Submission Package (e.g., a SEND dataset for a study) Study Plan or Protocol LIMS LIMS Non Standard Files Lab Data Study Reports Extract, Re-Purpose, Relate, Annotate Extract, Re-Purpose, Harmonize Naming Conventions & Terminologies Collection Gaps, Terminology Gaps Extract from Multiple Data Sources, Relate Across Data Sources, Normalize to Standardized Representation, Harmonize to Controlled Terminologies Data Definition File ( Define File) Trial (Protocol) Design Study Results e-data Package for Submission to the FDA Related Records, Comments Legend Manual Process Automated process based on data exchange interfaces Slide 11

12 Experience with e-data Submissions to Date One-off dataset compilation with a lot of manual intervention is possible at least by some sponsors & CROs Reliable and repeatable processes for routine compilation of submission ready e-datasets are non-existent Very few, if any, companies have submissions that successfully load in their entirety into NIMS Slide 12

13 Challenges - Expertise and Knowledge Gaps Understanding of Standardized e-data Models like SEND The SEND IG is open to subjective interpretation FDA Study Data Specification rules Requirements for Data Representation Data organization - where & how should data be best represented? Conventions nomenclature, date formats, Use of controlled terminologies Relating data represented in different categories or files Dealing with missing data or those that are hard to find & extract Companies can model the same kinds of datasets in different ways. This impacts both the FDA and Industry Slide 13

14 A Word on Controlled Terminologies (CTs) Collaborative effort among CDISC SEND Team, FDA and INHAND. FDA and STP are collaborating to adopt the INHAND nomenclature to standardize anatomic pathology findings scientific expects from INHAND, sponsors, CROs and the FDA are on the SEND CT team CT s include protocol elements, clinical signs, clinical pathology, & macroscopic and microscopic findings CT s include both fixed and extensible lists Important to note that the original findings can be submitted intact to the FDA in the current e-data models. CT s will allow for more effective cross-study analytics and signal detection Slide 14

15 Challenges Data Management & Collection Systems Study data can be scattered in as many as four sources: In-life, Histopathology, PK systems plus Protocol Design files Generating reliable data extracts from some data collection sources can be a challenge Same collection system exports different outputs for different studies! Relating data correctly across sources, often under GLP controls, and representing them in standardized formats requires new processes for data governance No magic recipe for success. Software is not a panacea to address fundamental data management or process issues. GIGO (Garbage In, Garbage Out) will be the result Slide 15

16 Challenges Beyond Standards like SEND Regulators and Sponsors both have an interest in building repositories of standardized e-data Applying analytics across Big Data for signal detection; increased efficiency; streamlining reporting & submission processes SEND is currently limited to General Tox & Carc Studies What about Safety Pharm, ReproTox, Medical Countermeasure, Novel Protocols, Other Study Types? Standards development is lengthy several years at best! Slide 16

17 Addressing Immediate Term Challenges Initiate your e-data standardization efforts ASAP and be both agile and flexible fly the plane as you build it! Deep dive into your own and CRO generated datasets Work through the full lifecycle of compiling data for selected studies across all of your data sources/systems to determine requirements for both tools and processes Review CRO generated SEND datasets with the same level of rigor sponsors are ultimately accountable for datasets sent to the FDA! Select vendors that combine expertise in tools & processes to compile complex data across disparate sources Get involved with initiatives like CDISC SEND & PhUSE Slide 17

18 Novel Ways to Accelerate Standardized e-data Representation MCM as an example Draft Guidance for Animal Models Essential Elements to Address Efficacy Under the Animal Rule released in Companies have started submitting studies to the FDA for approval of Medical Countermeasures (MCM). MCMs are needed to prevent or treat diseases or conditions caused by chemical, biological, radiological, or nuclear (CBRN) or emerging infectious disease threats. MCMs include medical products such as drugs, vaccines and/or combination therapies. Slide 18

19 What is unique about MCM studies? And why current standards like SEND are insufficient Exposures: Subjects administered two kinds of Interventions Change of state of a subject from a healthy state to an abnormal state by initial threat exposure and then again a reversal of state by administering MCM New Data Domains & Variables not typically in a tox study New domains and variables like Disease/Threat Agent Characteristics, Pathogens Excluded, Antimicrobials (Concomitant Medications), Medical History & Protocol Deviations among others must be included in the data Models for MCM studies Evolving study protocols require flexibility to further extend models Slide 19

20 What is unique about MCM studies? (Contd.) Study Designs based on Adaptive Responses of Subjects Many unplanned elements within the studies. E.g., The trigger or signals for intervention. Can vary from subject to subject. Need to cluster subjects based on threat and MCM dosages Assess efficacy in addition to safety Reporting of Findings Assessment of responses may not always be against a fixed reference point Slide 20

21 NIMS Data Model, Visualization & Analytics Extensions for MCM Studies 2012: The FDA initiated a NIMS extension for MCM studies Data model, visualization and analytics in NIMS extended to handle representation of complex MCM studies Selected studies submitted to the FDA have been standardized to this model and have been loaded by PointCross into NIMS NIMS models are also being extended based on standardization of study data by sponsors Within a few months, an entirely new and complex type of study has been successfully modeled. Agile, iterative modeling based on representative study data is an alternate to traditional standards development. Data schemas can eventually be published as a standard. Slide 21

22 Future e-protocol Design why it matters Standardized e-datasets today are being generated ex post without fundamental changes in protocol design and data management practices. Reliance on paper or PDFbased protocols does not fully realize the benefits of digital protocol designs. Clinical trials already use electronic case report forms (ecrf s) to specify and design data collection for the trial. If the industry adopts this concept for nonclinical studies: It is possible to represent planned & actual protocols, and amendments/deviations electronically across the lifecycle of studies Valuable for reviewers and researchers alike to evaluate results Can also ensure that collected data will fit into the eventual data representation for e-data submissions bringing greater efficiency. Slide 22

23 Conclusions Steep learning curve for the industry based on actual use cases is required there is no time to waste. A knowledge base about the issues sponsors and CROs need to confront is developing slowly. Software tools are only a means to the end; not a panacea for poor data management processes & practices! Developing data models for new study types using actual study data can accelerate standards development. Novel ways for electronic protocol design can increase efficiency and reduce the effort in e-data preparation Rapid adoption of e-data initiatives for regulatory submissions and R&D will benefit both the FDA & Industry Slide 23

24 Q&A Contacts: FDA PointCross Other Resources: CDISC SEND PhUSE Slide 24

25 Thank you for your participation in the American College of Toxicology Webinar! We hope to see you at the 34th Annual Meeting of the American College of Toxicology San Antonio Texas Hill Country, November 3 6, 2013 Slide 25

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